1
|
Igarashi Y, Kojima R, Matsumoto S, Iwata H, Okuno Y, Yamada H. Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method. J Toxicol Sci 2024; 49:117-126. [PMID: 38432954 DOI: 10.2131/jts.49.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.
Collapse
Affiliation(s)
- Yoshinobu Igarashi
- Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
| | - Shigeyuki Matsumoto
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
| | - Hiroaki Iwata
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University
| | - Hiroshi Yamada
- Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition
| |
Collapse
|
2
|
Yu Z, Wu Z, Zhou M, Cao K, Li W, Liu G, Tang Y. EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18013-18025. [PMID: 37053516 DOI: 10.1021/acs.est.2c08558] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.
Collapse
Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Kangjia Cao
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
3
|
Yang J, Jiang C, Chen J, Qin L, Cheng G. Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model. ChemistryOpen 2023; 12:e202300051. [PMID: 37404062 PMCID: PMC10661831 DOI: 10.1002/open.202300051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/23/2023] [Indexed: 07/06/2023] Open
Abstract
Recent studies have identified G protein-coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and suppression of glucagon levels. In this study, we constructed an up-to-date GPR40 ligand dataset for training models and performed a systematic optimization of the ensemble model, resulting in a powerful ensemble model (ROC AUC: 0.9496) for distinguishing GPR40 agonists and non-agonists. The ensemble model is divided into three layers, and the optimization process is carried out in each layer. We believe that these results will prove helpful for both the development of GPR40 agonists and ensemble models. All the data and models are available on GitHub. (https://github.com/Jiamin-Yang/ensemble_model).
Collapse
Affiliation(s)
- Jiamin Yang
- School of Pharmaceutical SciencesZhejiang Chinese Medical UniversityHangzhouP. R. China310053
| | - Chen Jiang
- School of Pharmaceutical SciencesZhejiang Chinese Medical UniversityHangzhouP. R. China310053
| | - Jing Chen
- School of Pharmaceutical SciencesZhejiang Chinese Medical UniversityHangzhouP. R. China310053
| | - Lu‐Ping Qin
- School of Pharmaceutical SciencesZhejiang Chinese Medical UniversityHangzhouP. R. China310053
| | - Gang Cheng
- School of Pharmaceutical SciencesZhejiang Chinese Medical UniversityHangzhouP. R. China310053
| |
Collapse
|
4
|
Zhang R, Chen Z, Wang B, Li Y, Mu Y, Li X. Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity. ACS OMEGA 2023; 8:31675-31682. [PMID: 37692239 PMCID: PMC10483523 DOI: 10.1021/acsomega.3c01725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts. The consensus model achieved good predictive results with high total accuracy at 87.21% for validation sets. The models can be accessed freely via https://ochem.eu/article/158582. Besides, several commonly used chemical properties were significantly different between chemicals with and without mitochondrial toxicity. We also detected the structural alerts (SAs) responsible for mitochondrial toxicity and integrated them into the web-server SApredictor (www.sapredictor.cn). The study may provide useful tools for in silico estimation of mitochondrial toxicity and be helpful to understand the mechanisms of mitochondrial toxicity.
Collapse
Affiliation(s)
- Ruiqiu Zhang
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Zhaoyang Chen
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Baobao Wang
- Department
of Nephrology, The First Affiliated Hospital
of Shandong First Medical University & Shandong Provincial Qianfoshan
Hospital, Jinan 250014, China
| | - Yan Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| |
Collapse
|
5
|
Shin HK, Huang R, Chen M. In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review. Food Chem Toxicol 2023; 179:113948. [PMID: 37460037 PMCID: PMC10640386 DOI: 10.1016/j.fct.2023.113948] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/25/2023]
Abstract
New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.
Collapse
Affiliation(s)
- Hyun Kil Shin
- Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), 34114, Daejeon, Republic of Korea
| | - Ruili Huang
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR, 72079, USA.
| |
Collapse
|
6
|
Li L, Lu Z, Liu G, Tang Y, Li W. Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates. Chem Res Toxicol 2023; 36:1332-1344. [PMID: 37437120 DOI: 10.1021/acs.chemrestox.3c00065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.
Collapse
Affiliation(s)
- Longqiang Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhou Lu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
7
|
Hu Y, Ren Q, Liu X, Gao L, Xiao L, Yu W. In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods. Chem Res Toxicol 2023. [PMID: 37300507 DOI: 10.1021/acs.chemrestox.2c00411] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.
Collapse
Affiliation(s)
- Yuxuan Hu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Qiuhan Ren
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Xintong Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Liming Gao
- School of Science, China Pharmaceutical University, Nanjing 211198, China
| | - Lecheng Xiao
- School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Wenying Yu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| |
Collapse
|
8
|
Jaganathan K, Rehman MU, Tayara H, Chong KT. XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity. Int J Mol Sci 2022; 23:ijms232415655. [PMID: 36555297 PMCID: PMC9779353 DOI: 10.3390/ijms232415655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity.
Collapse
Affiliation(s)
- Keerthana Jaganathan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Mobeen Ur Rehman
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Correspondence: (H.T); (K.T.C)
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Correspondence: (H.T); (K.T.C)
| |
Collapse
|
9
|
Shen Y, Guo K, Ma A, Huang Z, Du J, Chen J, Lin Q, Wei C, Wang Z, Zhang F, Zhang J, Lin W, Feng N, Ma W. Mitochondrial toxicity evaluation of traditional Chinese medicine injections with a dual in vitro approach. Front Pharmacol 2022; 13:1039235. [DOI: 10.3389/fphar.2022.1039235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Graphical AbstractA dual in vitro mitochondrial toxicity assay approach combing the conventional “glucose/galactose” assay in HepG2 cells with the cytotoxic assay in mitochondrial respiration deficient cells was established in this study. Using this platform, we systematically assessed the mitochondrial toxicity of TCM injections for the first time. Four TCM injections were identified with potential mitochondrial toxicity. Their toxic ingredients were predicted by molecular docking and validated by the dual in vitro approach.
Collapse
|
10
|
Zhang J, Cui S, Shen L, Gao Y, Liu W, Zhang C, Zhuang S. Promotion of Bladder Cancer Cell Metastasis by 2-Mercaptobenzothiazole via Its Activation of Aryl Hydrocarbon Receptor Transcription: Molecular Dynamics Simulations, Cell-Based Assays, and Machine Learning-Driven Prediction. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13254-13263. [PMID: 36087060 DOI: 10.1021/acs.est.2c05178] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
2-Mercaptobenzothiazole (MBT) is an industrial chemical widely used for rubber products, corrosion inhibitors, and polymer materials with multiple environmental and exposure pathways. A growing body of evidence suggests its potential bladder cancer (BC) risk as a public health concern; however, the molecular mechanism remains poorly understood. Herein, we demonstrate the activation of the aryl hydrocarbon receptor (AhR) by MBT and reveal key events in carcinogenesis associated with BC. MBT alters conformational changes of AhR ligand binding domain (LBD) as revealed by 500 ns molecular dynamics simulations and activates AhR transcription with upregulation of AhR-target genes CYP1A1 and CYP1B1 to approximately 1.5-fold. MBT upregulates the expression of MMP1, the cancer cell metastasis biomarker, to 3.2-fold and promotes BC cell invasion through an AhR-mediated manner. MBT is further revealed to induce differentially expressed genes (DEGs) most enriched in cancer pathways by transcriptome profiling. The exposure of MBT at environmentally relevant concentrations induces BC risk via AhR signaling disruption, transcriptome aberration, and malignant cell metastasis. A machine learning-based model with an AUC value of 0.881 is constructed to successfully predict 31 MBT analogues. Overall, we provide molecular insight into the BC risk of MBT and develop an effective tool for rapid screening of AhR agonists.
Collapse
Affiliation(s)
- Jiachen Zhang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
- Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
| | - Shixuan Cui
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Lilai Shen
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yuchen Gao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Weiping Liu
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chunlong Zhang
- Department of Environmental Sciences, University of Houston-Clear Lake, 2700 Bay Area Boulevard, Houston, Texas 77058, United States
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
- Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
| |
Collapse
|
11
|
Li L, Lu Z, Liu G, Tang Y, Li W. In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods. Chem Res Toxicol 2022; 35:1614-1624. [PMID: 36053050 DOI: 10.1021/acs.chemrestox.2c00207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a few resources and time. In recent years, machine learning methods have made much progress. Therefore, development of computational models to predict liver microsomal stability is highly desirable in the drug discovery process. In this study, the in silico classification models for the prediction of the metabolic stability of compounds in rat and human liver microsomes were constructed by the conventional machine learning and deep learning methods. The performance of the models was evaluated using the test and external sets. For the rat liver microsomes (RLM) stability, the best model yielded the AUC values of 0.84 and 0.71 on the test and external validation sets, respectively. For the human liver microsome (HLM) stability, the best model exhibited the AUC values of 0.86 and 0.77 on the test and external validation sets, respectively. In addition, several important substructure fragments were detected using information gain and frequency substructure analysis methods. The applicability domain of the models was defined using the Euclidean distance-based method. We anticipate that our results would be helpful for the prediction of liver microsomal stability of compounds in the early stage of drug discovery.
Collapse
Affiliation(s)
- Longqiang Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhou Lu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
12
|
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection. Commun Biol 2022; 5:858. [PMID: 35999457 PMCID: PMC9399120 DOI: 10.1038/s42003-022-03763-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/25/2022] [Indexed: 12/05/2022] Open
Abstract
Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery. Cell Painting, gene expression, and chemical structural data are used to examine the differences between mitochondrial toxicants and non-toxicants and enhance the detection of mitotoxic compounds for future drug discovery.
Collapse
|
13
|
Trapotsi MA, Mouchet E, Williams G, Monteverde T, Juhani K, Turkki R, Miljković F, Martinsson A, Mervin L, Pryde KR, Müllers E, Barrett I, Engkvist O, Bender A, Moreau K. Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature. ACS Chem Biol 2022; 17:1733-1744. [PMID: 35793809 PMCID: PMC9295119 DOI: 10.1021/acschembio.2c00076] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.
Collapse
Affiliation(s)
- Maria-Anna Trapotsi
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.,Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Elizabeth Mouchet
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Guy Williams
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Tiziana Monteverde
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Karolina Juhani
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Riku Turkki
- Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Filip Miljković
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Anton Martinsson
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Kenneth R Pryde
- Oncology Safety, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Erik Müllers
- Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ian Barrett
- Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Kevin Moreau
- Safety Innovation, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| |
Collapse
|
14
|
Mihajlovic M, Vinken M. Mitochondria as the Target of Hepatotoxicity and Drug-Induced Liver Injury: Molecular Mechanisms and Detection Methods. Int J Mol Sci 2022; 23:ijms23063315. [PMID: 35328737 PMCID: PMC8951158 DOI: 10.3390/ijms23063315] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022] Open
Abstract
One of the major mechanisms of drug-induced liver injury includes mitochondrial perturbation and dysfunction. This is not a surprise, given that mitochondria are essential organelles in most cells, which are responsible for energy homeostasis and the regulation of cellular metabolism. Drug-induced mitochondrial dysfunction can be influenced by various factors and conditions, such as genetic predisposition, the presence of metabolic disorders and obesity, viral infections, as well as drugs. Despite the fact that many methods have been developed for studying mitochondrial function, there is still a need for advanced and integrative models and approaches more closely resembling liver physiology, which would take into account predisposing factors. This could reduce the costs of drug development by the early prediction of potential mitochondrial toxicity during pre-clinical tests and, especially, prevent serious complications observed in clinical settings.
Collapse
|
15
|
Wang Y, Wang B, Jiang J, Guo J, Lai J, Lian XY, Wu J. Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants. ACS OMEGA 2021; 6:26545-26555. [PMID: 34661009 PMCID: PMC8515573 DOI: 10.1021/acsomega.1c03842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/14/2021] [Indexed: 05/17/2023]
Abstract
Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.
Collapse
Affiliation(s)
- Yiwei Wang
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Binyou Wang
- School
of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Jie Jiang
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Jianmin Guo
- School
of Preclinical Medicine, Southwest Medical
University, Luzhou 646000, China
| | - Jia Lai
- School
of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Xiao-Yuan Lian
- School
of Pharmacy, Zhejiang University, Hangzhou 310011, China
| | - Jianming Wu
- Key
Laboratory of Medical Electrophysiology, Ministry of Education of
China, Medical Key Laboratory for Drug Discovery and Druggability
Evaluation of Sichuan Province, Luzhou Key
Laboratory of Activity Screening and Druggability Evaluation for Chinese
Materia Medica, Luzhou 646000, China
| |
Collapse
|